Information processing device

Abstract

To provide an information processing device capable of calculating an index value in a short period of time by extracting extraction data having obtained the feature of the entire original data.SOLUTION: A processing device of an information processing device executes retrieval processing which includes a first step (S120) of calculating a relative frequency distribution of original data, a second step (S125) of setting a plurality of time windows for cutting out a partial period data of the original data, a third step (S130) of cutting out data from the original data, a fourth step (S140) of calculating a relative frequency distribution of extraction data, and a fifth step (S145) for calculating an error between the relative frequency distribution of the original data and the relative frequency distribution of the extraction data, and repeatedly executes an attempt from the second step to the fifth step, changing settings of the plurality of time windows. The processing device calculates an index value of damage of a parking lock mechanism by using the extraction data whose error becomes equal to or smaller than the threshold value (S160).SELECTED DRAWING: Figure 4

Description

Technical Field

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[0001] This invention relates to an information processing apparatus. <000°005>

Background Art

[0002] Patent Document 1 discloses an information processing apparatus that reduces the size of analysis data by compressing original data for analysis. The original data for analysis is data collected over a predetermined period using sensors mounted on a vehicle.

[0003] The information processing apparatus disclosed in Patent Document 1 compresses data by extracting data from the original data, including data acquired when a certain vehicle speed is reached and data acquired at the inflection point of the vehicle speed.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0007] The above-described information processing device can calculate index values ​​using extracted data with the same accuracy as when using the original data. Therefore, the above-described information processing device can calculate index values ​​in a shorter time compared to when using the original data. [Brief explanation of the drawing]

[0008] [Figure 1] Figure 1 is a schematic diagram showing the relationship between a data center, which is one embodiment of an information processing device, a vehicle, and an information processing terminal. [Figure 2] Figure 2 is a perspective view of the parking lock mechanism. [Figure 3] Figure 3 is a graph showing a portion of the original data. [Figure 4] Figure 4 is a flowchart showing the processing flow executed by the processing unit in the data center. [Figure 5] Figure 5 shows the relative frequency distribution for vehicle tilt angle in the original data, illustrating the distribution of relative frequencies within the positive range classes. [Figure 6] Figure 6 shows the relative frequency distribution for vehicle tilt angle in the original data, illustrating the distribution of relative frequencies within the negative range classes. [Figure 7] Figure 7 shows the relative frequency distribution for vehicle tilt angle in the original data, indicating the relative frequency in the class range of 0. [Modes for carrying out the invention]

[0009] Below, a data center 500, which is one embodiment of an information processing device, will be described with reference to Figures 1 to 7. <Configuration of the Information Processing System> Figure 1 shows the configuration of an information processing system including a data center 500. As shown in Figure 1, the data center 500 communicates with the vehicles 10 via a communication network 400. The data center 500 also communicates with information processing terminals 600 via the communication network 400. The data center 500 communicates with multiple vehicles 10 and multiple information processing terminals 600 via the communication network 400.

[0010] <Data Center 500 Configuration> As shown in Figure 1, the data center 500 includes a processing unit 510. The data center 500 also includes a storage device 520 and a communication device 530. The processing unit 510 includes a CPU that executes processing according to a program and a ROM in which the program is stored. The storage device 520 stores a large amount of data. The communication device 530 is implemented as hardware such as a network adapter, various communication software, or a combination thereof. The communication device 530 enables wired or wireless communication via the communication network 400.

[0011] The data center 500 may be configured using multiple computers. For example, the data center 500 may be configured using multiple server devices. <Vehicle 10 configuration> Each of the multiple vehicles 10 is equipped with a communication device 80. These communication devices 80 are implemented as hardware such as network adapters, various communication software, or a combination thereof. These communication devices 80 are configured to enable wired or wireless communication via a communication network 400.

[0012] Each vehicle 10 is equipped with an engine 20 and an automatic transmission 30. For example, the automatic transmission 30 is a planetary gear type transmission. The automatic transmission 30 is equipped with a parking lock mechanism 40.

[0013] Vehicle 10 is equipped with an engine control device 50 and a transmission control device 60. The engine control device 50 controls the engine 20. The transmission control device 60 controls the automatic transmission 30. The parking lock mechanism 40 is also controlled by the transmission control device 60.

[0014] The engine control device 50 and the transmission control device 60 are equipped with various sensors that collect information from various parts of the vehicle 10. In each vehicle 10, driving data is collected from such various sensors. From each vehicle 10, the driving data is transmitted to the data center 500 by the communication device 80. For example, the driving data including the driving distance, position information, and vehicle speed of each vehicle 10 is transmitted from each vehicle 10 to the data center 500. Further, the driving data also includes various data indicating the state of the automatic transmission 30 acquired by the transmission control device 60 of the vehicle 10. Further, the driving data also includes data indicating the state of the parking lock mechanism 40. The identification information for identifying each vehicle 10 is also transmitted from each vehicle 10 to the data center 500 together with the driving data.

[0015] The data center 500 stores the driving data in the storage device 520 together with the received identification information. In this way, the driving data of a plurality of vehicles 10 is accumulated in the storage device 520 of the data center 500.

[0016] <Configuration of the parking lock mechanism 40> As shown in FIG. 2, the parking lock mechanism 40 includes a parking gear 41 and a lock pole 42 provided with a locking piece 46. The parking lock mechanism 40 is housed in the case of the automatic transmission 30. The parking gear 41 is fixed to the output shaft of the automatic transmission 30 that is interlocked with the drive wheels. The lock pole 42 is attached to the case of the automatic transmission 30 so as to rotate about the support shaft 45. The parking lock mechanism 40 mechanically restricts the rotation of the output shaft of the automatic transmission 30 by rotating the lock pole 42 toward the parking gear 41 about the support shaft 45 and engaging the locking piece 46 with the parking gear 41. Thereby, the parking lock mechanism 40 restricts the rotation of the drive wheels.

[0017] The parking lock mechanism 40 includes a parking gear 41, a lock pole 42, and a rod 44 driven by an actuator in addition to the lock pole 42. A tapered portion 43 that becomes thinner toward the tip is provided at the tip of the rod 44. The lock pole 42 is in contact with the tapered portion 43. In the parking lock mechanism 40, the rod 44 is moved in the axial direction by the actuator. Then, as the rod 44 moves axially, the lock pole 42 that is in contact with the tapered portion 43 rotates about the support shaft 45.

[0018] The state shown in FIG. 2 is a state in which the lock by the parking lock mechanism 40 is released. In this state, the rotation of the drive wheel is not restricted by the parking lock mechanism 40.

[0019] When the rod 44 is moved in the direction of the arrow shown in FIG. 2 by the actuator from this state, the lock pole 42 is pushed upward toward the parking gear 41 by the tapered portion 43. Then, when the locking piece 46 of the lock pole 42 is pushed upward to a position where it meshes with the parking gear 41, the rotation of the drive wheel that rotates in conjunction with the parking gear 41 is mechanically restricted.

[0020] In this way, the parking lock mechanism 40 realizes a parking lock that restricts the rotation of the drive wheel by engaging the lock pole 42 with the parking gear 41. When the rod 44 is moved in the direction opposite to the arrow shown in FIG. 2 by the actuator from the state where the parking lock is applied, the lock pole 42 that has been pushed up by the tapered portion 4 becomes lower. As a result, the locking piece 46 of the lock pole 42 separates from the parking gear 41 and the parking lock is released.

[0021] <Configuration of the information processing terminal 600> The information processing terminal 600 comprises a processing unit 610, a storage device 620, and a communication device 630. The processing unit 610 includes a CPU that executes processing according to a program and a ROM in which the program is stored. The storage device 620 stores data. The communication device 630 is implemented as hardware such as a network adapter, various communication software, or a combination thereof. The communication device 630 enables wired or wireless communication via the communication network 400. The information processing terminal 600 is, for example, a personal computer.

[0022] <Analysis of driving data for vehicle 10> The information processing terminal 600 is used for analyzing driving data. When analyzing driving data, the information processing terminal 600 sends an instruction to the data center 500 to perform the analysis. Upon receiving the instruction, the processing unit 510 of the data center 500 uses a portion of the vast amount of driving data stored in the data center 500's storage device 520 to perform the analysis. The driving data to be used is selected from the vast amount of driving data stored in the storage device 520 according to the purpose of the analysis.

[0023] For example, the processing unit 510 calculates the load on a specific part of a specific vehicle 10 based on the driving data of that specific vehicle 10. Based on the calculated load, the processing unit 510 estimates the damage accumulated in that part. For example, based on the driving data of the specific vehicle 10, the processing unit 510 calculates an index value indicating the magnitude of the damage accumulated in the parking lock mechanism 40 of the specific vehicle 10. The processing unit 510 in the data center 500 outputs the calculated result by transmitting it to the information processing terminal 600. The information processing terminal 600, upon receiving the result, displays the received result.

[0024] To perform this analysis, the processing unit 510 analyzes a large amount of driving data collected over a long period of time. Because the processing unit 510 needs to perform a massive amount of calculations, the analysis takes a long time.

[0025] Therefore, it is conceivable to extract data that captures the characteristics of the entire original data from the large amount of original driving data. If such extracted data can be obtained, the processing unit 510 can perform analysis in a shorter time by using the extracted data. For example, when estimating the damage to the parking lock mechanism 40 after 100,000 hours of use, the processing unit 510 estimates the damage using 20,000 hours of extracted data extracted from the 100,000 hours of original data. In this embodiment, the 100,000 hours of original data is data extracted from the driving data for the period during which the parking lock mechanism 40 was in operation. The processing unit 510 then calculates an index value of the damage to the parking lock mechanism 40 after 100,000 hours of use by multiplying the index value calculated from the 20,000 hours of extracted data extracted from the 100,000 hours of original data by 5.

[0026] Figure 3 shows a portion of the original data. The original data shown in Figure 3 represents 100,000 hours of original data from a single vehicle 10. The original data shown in Figure 3 includes data on the tilt angle of vehicle 10 as a feature. A positive tilt angle of vehicle 10 indicates that vehicle 10 is on an uphill slope. A negative tilt angle of vehicle 10 indicates that vehicle 10 is on a downhill slope. The tilt angle of vehicle 10 can be detected by a G-sensor mounted on vehicle 10. A G-sensor is a sensor that detects acceleration. For example, the G-sensor is a 3-axis accelerometer that can detect acceleration in the longitudinal direction, lateral direction, and vertical direction of vehicle 10.

[0027] Figure 3 shows data in chronological order of the tilt angle of the vehicle 10 when the parking lock mechanism 40 is in use, that is, when the lock pawl 42 is engaged with the parking gear 41 in the parking lock mechanism 40.

[0028] The tilt angle of the vehicle 10 when the lock pawl 42 is engaged with the parking gear 41 correlates with the damage to the parking lock mechanism 40. The processing unit 510 estimates the damage to the parking lock mechanism 40 using extracted data, which includes the data on the tilt angle of the vehicle 10 when the lock pawl 42 is engaged with the parking gear 41, as a feature.

[0029] Extracted data is created by cutting out data from the original data using multiple time windows. In Figure 3, three time windows—the first time window W_1, the second time window W_2, and the third time window W_3—are shown as examples of multiple time windows, each represented by a dashed line. The start and end dates of each time window are set so that they do not overlap. In this example, 20,000 hours of data are extracted. Therefore, the start and end dates of each time window are set so that the total length of the periods of all time windows combined is 20,000 hours.

[0030] Data center 500 searches for start and end date settings for each time window that represent extraction patterns for extracting data that captures the characteristics of the entire original data. Data Center 500 extracts data from the original data using the extraction patterns found through exploration. Data Center 500 then performs analysis using the extracted data.

[0031] <Searching for extraction patterns> Figure 4 is a flowchart showing the sequence of processes related to the extraction pattern search process. This sequence of processes is performed by the processing unit 510 of the data center 500.

[0032] As shown in Figure 4, the processing unit 510 acquires original data in step S100. The original data is a portion of the driving data selected from the vast amount of driving data stored in the storage device 520 of the data center 500 according to the purpose of the analysis. For example, the original data for calculating an index value indicating the magnitude of damage accumulated in the parking lock mechanism 40 of one vehicle 10 is the driving data of the target vehicle 10 over a predetermined period, selected from the vast amount of driving data of multiple vehicles 10. For example, the processing unit 510 estimates the damage to the parking lock mechanism 40 after 100,000 hours of use. In this case, the original data is the data extracted from the driving data of the target vehicle 10 over a predetermined period, specifically the data for the period during which the parking lock mechanism 40 was in use, totaling 100,000 hours.

[0033] Next, in the processing of step S120, the processing unit 510 calculates the relative frequency distribution of the original data. As described above, the original data includes data on the tilt angle of the vehicle 10 as a feature. The processing unit 510 calculates the relative frequency distribution of the tilt angle of the vehicle 10 in the original data.

[0034] A frequency distribution classifies data into multiple classes and represents the distribution of the number of data points in each class. Relative frequency indicates what percentage of the total sum of frequencies a particular class represents.

[0035] The processing unit 510 divides the calculated relative velocity distribution into relative frequency distributions for classes in the positive range, relative frequency distributions for classes in the negative range, and relative frequency distributions for classes in the 0 range, and uses them accordingly. Figure 5 shows the relative frequency distribution of the positive range classes for the tilt angle of vehicle 10 in the original data shown in Figure 3. In this relative frequency distribution, the tilt angle classes in the original data are divided into m classes up to "m", with classes in the range of 0 being "1". In Figure 5, the classes on the right correspond to classes with larger absolute values ​​of the tilt angle.

[0036] Figure 6 shows the relative frequency distribution for the negative range of the tilt angle of vehicle 10 in the original data shown in Figure 3. In this relative frequency distribution, the tilt angle in the original data is divided into m classes, with classes in the range of 0 being "1" and the relative frequency distribution shown up to "m". In Figure 6, the classes on the right have larger absolute values ​​of the tilt angle.

[0037] Figure 7 shows the relative frequency distribution for the tilt angle of vehicle 10 in the original data shown in Figure 3, specifically for classes in the range of 0. In this relative frequency distribution, all relative frequencies except for the "1" class are 0.

[0038] In step S120, the processing unit 510 calculates this relative frequency distribution for the tilt angle of the vehicle 10 in the original data. As shown in Figures 5 to 7, the number of classes in each relative frequency distribution is the same.

[0039] Next, in the processing of step S125, the processing unit 510 sets multiple time windows in order to extract extracted data from the original data. Figure 3 shows an example of multiple time windows, consisting of three time windows W_1, W_2, and W_3. In the example shown in Figure 3, the duration of each time window is equal. As shown in Figure 3, the data extracted by each extraction window is feature data for the same period.

[0040] In step S125, the processing unit 510 randomly sets multiple time windows such that the sum of the periods of all time windows is shorter than the total period of the original data. As will be described later, the processing unit 510 combines all the data extracted using the multiple time windows set here to create extracted data. The sum of the periods of all time windows is a value that determines the capacity of the extracted data. Therefore, the sum of the periods of all time windows is set in advance.

[0041] For example, each time the processing unit 510 executes the process in step S125, it randomly sets the number of time windows, the start date of each time window, and the end date of each time window. At this time, the processing unit 510 sets each time window so that they do not overlap. In this way, the processing unit 510 randomly sets multiple time windows so that the sum of the periods of all time windows equals a predetermined period. In the process in step S125, the processing unit 510 may set multiple time windows by fixing the period of each time window to a constant value, as shown in Figure 3. In the process in step S125, the processing unit 510 may set multiple time windows by fixing the number of multiple time windows to a constant value.

[0042] In this way, by setting multiple time windows through the process in step S125, an extraction pattern for extracting data from the original data is determined. Once the extraction pattern is determined, the processing unit 510 proceeds to step S130.

[0043] In step S130, the processing unit 510 extracts data from the original data according to the determined extraction pattern. In other words, in step S130, the processing unit 510 extracts data from the original data according to multiple set time windows. Then, the processing unit 510 combines all the data extracted according to the multiple time windows to create extracted data.

[0044] In the next step, S140, the processing unit 510 calculates the relative frequency distribution of the extracted data. The processing unit 510 calculates the relative frequency distribution of the extracted data in the same way as the method used to calculate the relative frequency distribution in step S120. That is, in the processing of step S140, the processing unit 510 calculates the relative frequency distribution of the slope angle in the extracted data. At this time, the processing unit 510 makes the number of classes in the relative frequency distribution the same as the relative frequency distribution in step S120.

[0045] Next, in step S145, the processing unit 510 calculates the error between the relative frequency distribution in the original data and the relative frequency distribution in the extracted data. For example, the processing unit 510 calculates the Mean Absolute Error (MAE). The Mean Absolute Error (MAE) is expressed by the following formula.

[0046]

number

[0047] As shown in the formula above, the processing unit 510 calculates the error as the sum of the errors in the frequency of each class of the feature between the relative frequency distribution in the entire original data and the relative frequency distribution in the extracted data.

[0048] Once the error is calculated, the processing unit 510 proceeds to step S150. In step S150, the processing unit 510 determines whether the calculated error is less than or equal to a threshold. The threshold is a value used to determine whether extracted data having a relative frequency distribution similar to the relative frequency distribution in the original data has been extracted using the set extraction pattern. The magnitude of this threshold is pre-set so that, based on the error being less than or equal to the threshold, it can be determined that extracted data having a relative frequency distribution similar to the relative frequency distribution in the original data has been extracted.

[0049] If, in the process of step S150, it is determined that the error is below a threshold (step S150: YES), the processing unit 510 proceeds to step S160. In step S160, the processing unit 510 calculates a target index value using the extracted data created in the most recent step S130. Here, the fatigue damage degree is calculated as an index value indicating the magnitude of damage accumulated in the parking lock mechanism 40.

[0050] The greater the tilt angle of the vehicle 10 when the lock pawl 42 is engaged with the parking gear 41, the greater the damage accumulated in the parking lock mechanism 40. Furthermore, the location, direction, and magnitude of stress acting on the vehicle differ depending on whether the vehicle 10 is parked on an uphill or downhill slope. Therefore, the processing unit 510 calculates the degree of fatigue damage to the parking lock mechanism 40 based on the tilt angle, using different calculation methods for positive and negative tilt angles.

[0051] The fatigue damage level is an index value representing the percentage of accumulated fatigue, assuming that the damage to the parking lock mechanism 40 follows a linear cumulative damage law, also known as Minor's law, with the fatigue leading to damage set as "1". Here, the damage inflicted on the parking lock mechanism 40 over a certain period is calculated from the inclination angle. Then, the magnitude of the damage that leads to damage to the friction material due to a single load input is set as "1", and the calculated percentage of damage is used as the fatigue index value. By repeating this process, the calculated fatigue index values ​​are accumulated to calculate the fatigue damage level, which is the percentage of accumulated fatigue relative to the fatigue that leads to damage. When the fatigue damage level reaches "1", it means that damage has occurred, and the calculated fatigue damage level is a value between "0" and "1".

[0052] Here, since the fatigue damage level is calculated using extracted data, which is part of the original data, the processing unit 510 converts the calculated fatigue damage level to a size corresponding to the original data and calculates the fatigue damage level as an index value. For example, if the original data is 100,000 hours' worth of data and the extracted data is 20,000 hours' worth of data, the calculated fatigue damage level is multiplied by 5 to obtain the fatigue damage level as an index value.

[0053] On the other hand, if the processing in step S150 determines that the error is greater than the threshold (step S150: NO), the processing unit 510 returns to step S125. Then, the processing unit 510 executes the search process from step S125 to step S145 again.

[0054] In this way, the processing unit 510 repeatedly executes the search process in steps S125 to S145 by changing the settings of multiple time windows, and extracts data from the original data in which the error is below a threshold. Then, in the process of step S160, the processing unit 510 calculates the fatigue damage degree using the extracted data. Once the fatigue damage degree is calculated, the processing unit 510 proceeds to step S170.

[0055] In step S170, the processing unit 510 determines whether the fatigue damage level is above a predetermined value. The predetermined value is a value used to predict that the likelihood of damage occurring is high based on the fatigue damage level being above a predetermined value. For example, here, the default value for the fatigue damage level can be set to "0.9". In this case, it is possible to predict that the likelihood of damage occurring is high, based on the fact that 90% of the fatigue leading to damage has been reached.

[0056] In step S170, if it is determined that the fatigue damage level is equal to or greater than a predetermined value (step S170: YES), the processing unit 510 proceeds to step S180. In step S180, the processing unit 510 outputs the fatigue damage level and the failure prediction. Specifically, the processing unit 510 sends the fatigue damage level and the failure prediction to the information processing terminal 600 that sent the instruction requesting analysis.

[0057] Failure prediction is, for example, a message indicating that a failure has been predicted. In this way, the processing unit 510 issues a notification indicating that a failure has been predicted if the calculated fatigue damage level is above a predetermined value. Failure prediction may also be information about the lifespan until a failure occurs. For example, if the fatigue damage level calculated using extracted data from 100,000 hours of original data is an index value, the processing unit 510 calculates the running time until the fatigue damage level reaches "1" and outputs it as lifespan information. Lifespan information may also be converted to running distance based on the running distance of 100,000 hours and output.

[0058] In step S170, if it is determined that the fatigue damage level is less than a predetermined value (step S170: NO), the processing unit 510 proceeds to step S190. In step S190, the processing unit 510 outputs the fatigue damage level. Specifically, the processing unit 510 sends the fatigue damage level to the information processing terminal 600 that sent the instruction to request analysis.

[0059] When the processing in step S180 or step S190 is executed, the processing unit 510 terminates this series of processes. <Operation of this embodiment> The data center 500, which is an information processing device in this embodiment, acquires original data created by collecting data over a predetermined period of time using multiple sensors mounted on the vehicle 10, and calculates an index value indicating the magnitude of damage accumulated in the parking lock mechanism 40.

[0060] The data center 500 includes a processing unit 510 that performs processing. The original data includes, as features, data on the tilt angle of the vehicle 10 when the lock pawl 42 is engaged with the parking gear 41 in the parking lock mechanism 40. In this data center 500, the search process performed by the processing unit 510 includes a first step (step S120) of calculating the relative frequency distribution in the original data for the features included in the original data. The search process includes a second step (step S125) of setting up multiple time windows to extract data from a portion of the original data such that the sum of the periods of all time windows is shorter than the period of the entire original data. The search process includes a third step (step S130) of extracting data from the original data using the multiple time windows. The search process includes a fourth step (step S140) of calculating the relative frequency distribution in the extracted data obtained by combining all the data extracted using the multiple time windows. The search process includes a fifth step (step S145) in which the error between the relative frequency distribution in the original data and the relative frequency distribution in the extracted data is calculated. After executing the first step, the processing unit 510 executes a search process in which it repeatedly performs trials from the second to the fifth step by changing the settings of multiple time windows. The processing unit 510 then extracts data in which the error is below a threshold. The processing unit 510 calculates the fatigue damage degree as an index value using the extracted data in which the error is below a threshold (step S160).

[0061] This data center 500 allows us to obtain extracted data that captures the characteristics of the entire original data. Therefore, this data center 500 can use the extracted data to calculate index values ​​with the same accuracy as when using the original data.

[0062] <Effects of this embodiment> (1) According to the data center 500, which is an information processing device of this embodiment, the index value can be calculated in a shorter time compared to when using the original data.

[0063] (2) The processing unit 510 calculates the error by dividing the relative frequency distribution for the tilt angle into relative frequency distributions for classes in the positive range, relative frequency distributions for classes in the negative range, and relative frequency distributions for classes in the 0 range. The processing unit 510 then calculates an index value by distinguishing between the damage when the tilt angle is a positive value and the damage when the tilt angle is a negative value.

[0064] The direction of stress acting on each part of the parking lock mechanism 40 when the lock pawl 42 is engaged with the parking gear 41, and the parts on which the stress acts, differ depending on whether the vehicle 10's tilt angle is positive or negative. Therefore, the amount of damage accumulated in the parking lock mechanism 40 when the vehicle 10's tilt angle is positive differs from the amount of damage accumulated in the parking lock mechanism 40 when the vehicle 10's tilt angle is negative.

[0065] The data center 500 described above calculates index values ​​by distinguishing between damage when the vehicle 10's tilt angle is a positive value and damage when the vehicle 10's tilt angle is a negative value. Therefore, the data center 500 can calculate index values ​​more accurately compared to a case where the index values ​​are calculated without distinguishing between damage when the tilt angle is a positive value and damage when the tilt angle is a negative value.

[0066] (3) The data center 500 calculates the error by dividing the relative frequency distribution for the tilt angle into relative frequency distributions for positive classes, relative frequency distributions for negative classes, and relative frequency distributions for classes in the range of 0. Therefore, the data center 500 can calculate the index value using extracted data in which the relative frequency distribution for each range is close to that of the original data.

[0067] (4) The processing unit 510 terminates the search process when it has extracted one data point whose error is below the threshold, and calculates an index value using the extracted data point whose error is below the threshold. Therefore, the data center 500 can calculate the index value as soon as it has extracted one data point whose error is below the threshold, and output the results quickly.

[0068] (5) If the calculated indicator value is greater than or equal to a predetermined value (step S170: YES), the processing unit 510 issues a notification indicating that it has predicted the occurrence of a failure. Therefore, the data center 500 can notify the user that a failure has been predicted before the failure occurs.

[0069] (6) The processing unit 510 calculates the degree of fatigue damage as an indicator value. As a result, the data center 500 can inform the user how much time it has before failure occurs.

[0070] <Example of changes> This embodiment can be implemented with the following modifications. This embodiment and the following modifications can be combined with each other to the extent that they do not contradict each other technically.

[0071] In the data center 500 of the above embodiment, the processing unit 510 calculates an index value by distinguishing between damage when the tilt angle is a positive value and damage when the tilt angle is a negative value. Alternatively, the processing unit 510 may calculate an index value by estimating the damage based on the magnitude of the tilt angle in the same way in both cases, without distinguishing between damage when the tilt angle is a positive value and damage when the tilt angle is a negative value.

[0072] In the above embodiment, an example was shown in which the information processing device is implemented as a data center 500. An example was shown in which the calculation of the index value is performed in the data center 500. In contrast, the above information processing device may be implemented as an information processing terminal 600. In this case, the calculation of the index value is performed by the processing device 610 of the information processing terminal 600. The above information processing device may also be implemented as a control device of the vehicle 10. In this case, the calculation of the index value can also be performed by the control device of the vehicle 10. For example, the calculation of the index value can also be performed by the transmission control device 60 of the vehicle 10.

[0073] The above embodiment shows an example of extracting one data point and calculating an index value. In contrast, multiple data points may be extracted, and the final index value may be determined using multiple index values ​​calculated from each data point. For example, the minimum value, maximum value, mode, and mean may be used as the final index value. Alternatively, multiple index values ​​may be output.

[0074] • In the above embodiment, an example was shown in which a notification is given that a failure has been predicted when the index value is greater than or equal to a predetermined value. This may be omitted. After calculating the index value, only the processing in step S190 may be executed to output only the index value.

[0075] • While fatigue damage level was used as an example of an indicator value to be calculated, the indicator value to be calculated is not limited to fatigue damage level. The method for determining the time window setting in the cropping pattern does not have to be random. The setting of the time window in the cropping pattern can be changed according to a pre-defined rule, and the trial can be repeated.

[0076] The error calculated in step S145 is not limited to the mean absolute error (MAE). For example, the processing unit 510 may calculate the mean squared error as the error. The processing unit 510 may also calculate the root mean squared error as the error.

[0077] The original data may include information as a feature whether or not the handbrake is being used. If the handbrake is being used, the damage to the parking lock mechanism 40 will be less. Therefore, by also referring to the information on whether or not the handbrake is being used, the damage to the parking lock mechanism 40 can be estimated more accurately. [Explanation of Symbols]

[0078] 10...Vehicle, 20...Engine, 30...Automatic transmission, 40...Parking lock mechanism, 41...Parking gear, 42...Lock pawl, 43...Tapered section, 44...Rod, 45...Support shaft, 46...Locking piece, 50...Engine control device, 60...Transmission control device, 80...Communication device, 400...Communication network, 500...Data center, 510...Processing device, 520...Storage device, 530...Communication device, 600...Information processing terminal, 610...Processing device, 620...Storage device, 630...Communication device

Claims

1. This information processing device acquires original data created by collecting data over a predetermined period using multiple sensors mounted on the vehicle, and calculates an index value indicating the magnitude of damage accumulated in the parking lock mechanism. It includes a processing unit that performs processing, The aforementioned original data includes, as a feature, data on the vehicle's tilt angle when the lock pawl is engaged with the parking gear in the parking lock mechanism. The aforementioned processing device The search process includes: a first step of calculating the relative frequency distribution in the original data for the features contained in the original data; a second step of setting multiple time windows to extract data for a portion of the original data such that the sum of the periods of all time windows is shorter than the period of the entire original data; a third step of extracting data from the original data using the multiple time windows; a fourth step of calculating the relative frequency distribution in the extracted data obtained by combining all the data extracted using the multiple time windows; and a fifth step of calculating the error between the relative frequency distribution in the original data and the relative frequency distribution in the extracted data. After performing the first step, the search process is repeated by changing the settings of the multiple time windows and extracting the extracted data for which the error is less than or equal to a threshold. The process involves calculating the index value using the extracted data in which the error is below a threshold, and performing the following: Information processing device.

2. The processing device calculates the error by dividing the relative frequency distribution for the tilt angle into a relative frequency distribution for classes in the positive range, a relative frequency distribution for classes in the negative range, and a relative frequency distribution for classes in the zero range. The index value is calculated by distinguishing between the damage when the inclination angle is a positive value and the damage when the inclination angle is a negative value. The information processing apparatus according to claim 1.

3. The processing device terminates the search process when it has extracted one data item whose error is below the threshold, and calculates the index value using the extracted data item whose error is below the threshold. The information processing apparatus according to claim 1.

4. If the calculated index value is greater than or equal to a predetermined value, the processing device will issue a notification indicating that a malfunction has been predicted. The information processing apparatus according to claim 1.

5. The processing apparatus calculates the degree of fatigue damage as the index value. The information processing apparatus according to claim 1.

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